Oral health in the daily lives of individuals allows health promotion and prevention. Oral health literacy is fundamental to enable people to have the knowledge and be aware of its importance, enabling them to process, evaluate and apply information on this topic. Serious games, in the educational domain, can be used to assist in this literacy. This article carries out systematic mapping of these games in the field of oral health. The mapping included work carried out in the last 5 years and indexed in four repositories. As result it was found that there are few serious games for oral health, and few have a well-defined process for learning.

Hierarchical classification is a problem with applications in many areas as protein function prediction where the dates are hierarchically structured. Therefore, it is necessary the development of algorithms able to induce hierarchical classification models. This paper presents an algorithm for hierarchical classification using the global approach, called Multilabel Hierarchical Classification using a Competitive Neural Network (MHC-CNN). It was tested on some datasets from the bioinformatics field and its results are promising.

Dimensionality reduction applied to gene expression is challenging for machine learning algorithms due to a small number of samples and a high number of attributes. This paper proposes a preprocessing phase by means of random projection method in microarray data. Experimental results are promising and it shows that the use of this method improves the performance of classification algorithms.

Many times, when studying gene expression data, unknown attributes, which can be redundant and even, in certain cases, irrelevant, are manipulated. The application of selection attributes algorithms as a preprocessing can help in the knowledge discovery database process. This paper is about applying selection attributes algorithms in two gene expression databases. The result shows that the use of these algorithms can improve the classification algorithms performance.

The use of data mining techniques has helped to solve many problems in the rapidly growing field of bioinformatics. Despite that, the presence of thousands of attributes makes the results unclear and also contributes to the decrease of the accuracy of the classifier used. This paper presents a comparison of the use of various attribute selection methods aiming to reduce the number of genes to be searched. The results show that most of the combinations from search algorithms and evaluation algorithms within the attribute selection algorithm work well, reducing the number of attributes and leading to improved classification rates.